Subject |
Statistical astronomy.
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Astronomy -- Data processing.
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Python (Computer program language)
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Descript |
1 online resource (x, 540 pages) : illustrations. |
Content |
text txt |
Media |
computer c |
Carrier |
online resource cr |
Contents |
I. Introduction -- 1. About the Book and Supporting Material -- 1.1. What do Data Mining, Machine Learning, and Knowledge Discovery mean? -- 1.2. What is this book about? -- 1.3. An incomplete survey of the relevant literature -- 1.4. Introduction to the Python Language and the Git Code Management Tool -- 1.5. Description of surveys and data sets used in examples -- 1.6. Plotting and visualizing the data in this book -- 1.7. How to efficiently use this book -- References -- 2. Fast Computation on Massive Data Sets -- 2.1. Data types and Data Management systems -- 2.2. Analysis of algorithmic efficiency -- 2.3. Seven types of computational Problem[s] -- 2.4. Seven strategies for speeding things up -- 2.5. Case studies: Speedup strategies in practice -- References. |
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II. Statistical Frameworks and Exploratory Data Analysis -- 3. Probability and Statistical Distributions -- 3.1. Brief overview of probability and random variables -- 3.2. Descriptive statistics -- 3.3. Common Univariate Distribution Functions -- 3.4. The Central Limit Theorem -- 3.5. Bivariate and Multivariate Distribution Functions -- 3.6. Correlation coefficients -- 3.7. Random number generation for arbitrary distributions -- References -- 4. Classical Statistical Inference -- 4.1. Classical vs. Bayesian Statistical Inference -- 4.2. Maximum Likelihood Estimation (MLE) -- 4.3. The goodness of Fit and Model Selection -- 4.4. ML Applied to Gaussian Mixtures: The Expectation Maximization Algorithm -- 4.5. Confidence estimates: the bootstrap and the jackknife -- 4.6. Hypothesis testing -- 4.7. Comparison of distributions -- 4.8. Nonparametric modeling and histograms -- 4.9. Selection effects and Luminosity Function Estimation -- 4.10. Summary -- References -- 5 Bayesian Statistical Inference -- 5.1. Introduction to the Bayesian method -- 5.2. Bayesian priors -- 5.3. Bayesian parameter uncertainty quantification -- 5.4. Bayesian model selection -- 5.5. Nonuniform priors: Eddington, Malmquist, and Lutz-Kelker biases -- 5.6. Simple examples of Bayesian analysis: Parameter estimation -- 5.7. Simple examples of Bayesian analysis: Model selection -- 5.8. Numerical methods for complex problems (MCMC) -- 5.9. Summary of pros and cons for classical and Bayesian methods -- References. |
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III. Data Mining and Machine Learning -- 6 Searching for Structure in Point Data -- 6.1. Nonparametric density estimation -- 6.2. Nearest-neighbor density estimation -- 6.3. Parametric density estimation -- 6.4. Finding clusters in data -- 6.5. Correlation functions -- 6.6. Which density estimation and clustering algorithms should I use? -- References -- 7 Dimensionality and its reduction -- 7.1. The curse of dimensionality -- 7.2. The data sets used in this chapter -- 7.3. Principal component analysis -- 7.4. Nonnegative matrix factorization -- 7.5. Manifold learning -- 7.6. Independent component analysis and projection pursuit -- 7.7. Which dimensionality reduction technique should I use? -- References -- 8 Regression and model fitting -- 8.1. Formulation of the regression problem -- 8.2. Regression for linear models -- 8.3. Regularization and penalizing the likelihood -- 8.4. Principal component regression -- 8.5. Kernel regression -- 8.6. Locally linear regression -- 8.7. Nonlinear regression -- 8.8. Uncertainties in the data -- 8.9. Regression that is robust to outliers -- 8.10. Gaussian process regression -- 8.11. Overfitting, underfitting, and cross-validation -- 8.12. Which regression method should I use? -- References. |
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III. Data Mining and Machine Learning (continued) -- 9 Classification -- 9.1. Data sets used in this chapter -- 9.2. Assigning categories: Classification -- 9.3. Generative classification -- 9.4. K-nearest-neighbor classifier -- 9.5. Discriminative classification -- 9.6. Support vector machines -- 9.7. Decision trees -- 9.8. Evaluating classifiers: ROC Curves -- 9.9. Which classifier should I use? -- References -- 10 Time Series Analysis -- 10.1. Main concepts for Time Series Analysis -- 10.2. Modeling toolkit for Time Series Analysis -- 10.3. Analysis of Periodic Time Series -- 10.4. Temporally localized signals -- 10.5. Analysis of Stochastic Processes -- 10.6. Which method should I use for Time Series Analysis? -- References. |
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IV. Appendices -- A An Introduction to Scientific Computing with Python -- A.1. A brief history of Python -- A.2. The ScyPy universe -- A.3. Getting started with Python -- A.4. IPython: The basics of interactive computing -- A.5. Introduction to NumPy -- A.6. Visualization with Matplotlib -- A.7. Overview of useful NumPy/SciPy modules -- A.8. Efficient coding with Python and NumPy -- A.9. Wrapping existing code in Python -- A.10. Other resources -- B AstroML: Machine Learning for Astronomy -- B.1. Introduction -- B.2. Dependencies -- B.3. Tools included in AstroML v0.1 -- C Astronomical Flux Measurements and Magnitudes -- C.1. The definition of the specific flux -- C.2. Wavelength window function for astronomical measurements -- C.3. The astronomical magnitude systems -- D SQL Query for Downloading SDSS Data -- E Approximating the Fourier Transform with the FFT -- References. |
Note |
Unlimited number of concurrent users. UkHlHU |
Alt author |
Connolly, Andrew (Andrew J.)
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Vanderplas, Jacob T.
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Gray, Alexander (Alexander G.)
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ISBN |
9781400848911 (electronic bk.) |
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1400848911 (electronic bk.) |
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0691151687 |
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9780691151687 |
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9781306373845 (MyiLibrary) |
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1306373840 (MyiLibrary) |
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